{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spatial transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.ion()   # interactive mode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
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       "model_id": "7d71e41e80864d398ae6958873275e6a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
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       "model_id": "c979abda6ddb44a4a87957c60be35f0a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dad8a807972647c88fc79a1e516e95ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5efe016482e45eba216aa6d2883e6c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
      "Processing...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/conda-bld/pytorch_1591914855613/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Training dataset\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST(root='.', train=True, download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])), batch_size=64, shuffle=True, num_workers=4)\n",
    "# Test dataset\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST(root='.', train=False, transform=transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.1307,), (0.3081,))\n",
    "    ])), batch_size=64, shuffle=True, num_workers=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.conv2_drop = nn.Dropout2d()\n",
    "        self.fc1 = nn.Linear(320, 50)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    "\n",
    "        # Spatial transformer localization-network\n",
    "        self.localization = nn.Sequential(\n",
    "            nn.Conv2d(1, 8, kernel_size=7),\n",
    "            nn.MaxPool2d(2, stride=2),\n",
    "            nn.ReLU(True),\n",
    "            nn.Conv2d(8, 10, kernel_size=5),\n",
    "            nn.MaxPool2d(2, stride=2),\n",
    "            nn.ReLU(True)\n",
    "        )\n",
    "\n",
    "        # Regressor for the 3 * 2 affine matrix\n",
    "        self.fc_loc = nn.Sequential(\n",
    "            nn.Linear(10 * 3 * 3, 32),\n",
    "            nn.ReLU(True),\n",
    "            nn.Linear(32, 3 * 2)\n",
    "        )\n",
    "\n",
    "        # Initialize the weights/bias with identity transformation\n",
    "        self.fc_loc[2].weight.data.zero_()\n",
    "        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))\n",
    "\n",
    "    # Spatial transformer network forward function\n",
    "    def stn(self, x):\n",
    "        xs = self.localization(x)\n",
    "        xs = xs.view(-1, 10 * 3 * 3)\n",
    "        theta = self.fc_loc(xs)\n",
    "        theta = theta.view(-1, 2, 3)\n",
    "\n",
    "        grid = F.affine_grid(theta, x.size())\n",
    "        x = F.grid_sample(x, grid)\n",
    "\n",
    "        return x\n",
    "\n",
    "    def forward(self, x):\n",
    "        # transform the input\n",
    "        x = self.stn(x)\n",
    "\n",
    "        # Perform the usual forward pass\n",
    "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
    "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
    "        x = x.view(-1, 320)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.dropout(x, training=self.training)\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "\n",
    "\n",
    "model = Net().to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
    "\n",
    "\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % 500 == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item()))\n",
    "#\n",
    "# A simple test procedure to measure STN the performances on MNIST.\n",
    "#\n",
    "\n",
    "\n",
    "def test():\n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        test_loss = 0\n",
    "        correct = 0\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "\n",
    "            # sum up batch loss\n",
    "            test_loss += F.nll_loss(output, target, size_average=False).item()\n",
    "            # get the index of the max log-probability\n",
    "            pred = output.max(1, keepdim=True)[1]\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "        test_loss /= len(test_loader.dataset)\n",
    "        print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'\n",
    "              .format(test_loss, correct, len(test_loader.dataset),\n",
    "                      100. * correct / len(test_loader.dataset)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfs_home/sohyongs/anaconda3/envs/fastai2/lib/python3.7/site-packages/torch/nn/functional.py:3289: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
      "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n",
      "/nfs_home/sohyongs/anaconda3/envs/fastai2/lib/python3.7/site-packages/torch/nn/functional.py:3226: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
      "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.340148\n",
      "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.722331\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfs_home/sohyongs/anaconda3/envs/fastai2/lib/python3.7/site-packages/torch/nn/_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
      "  warnings.warn(warning.format(ret))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set: Average loss: 0.2120, Accuracy: 9401/10000 (94%)\n",
      "\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.372773\n",
      "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.322500\n",
      "\n",
      "Test set: Average loss: 0.1955, Accuracy: 9358/10000 (94%)\n",
      "\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.404475\n",
      "Train Epoch: 3 [32000/60000 (53%)]\tLoss: 0.171209\n",
      "\n",
      "Test set: Average loss: 0.0950, Accuracy: 9720/10000 (97%)\n",
      "\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.425620\n",
      "Train Epoch: 4 [32000/60000 (53%)]\tLoss: 0.183614\n",
      "\n",
      "Test set: Average loss: 0.1318, Accuracy: 9582/10000 (96%)\n",
      "\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.309304\n",
      "Train Epoch: 5 [32000/60000 (53%)]\tLoss: 0.630292\n",
      "\n",
      "Test set: Average loss: 0.0793, Accuracy: 9762/10000 (98%)\n",
      "\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.417217\n",
      "Train Epoch: 6 [32000/60000 (53%)]\tLoss: 0.136987\n",
      "\n",
      "Test set: Average loss: 0.0584, Accuracy: 9820/10000 (98%)\n",
      "\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.040551\n",
      "Train Epoch: 7 [32000/60000 (53%)]\tLoss: 0.073858\n",
      "\n",
      "Test set: Average loss: 0.0551, Accuracy: 9839/10000 (98%)\n",
      "\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.098632\n",
      "Train Epoch: 8 [32000/60000 (53%)]\tLoss: 0.083939\n",
      "\n",
      "Test set: Average loss: 0.0590, Accuracy: 9809/10000 (98%)\n",
      "\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.123724\n",
      "Train Epoch: 9 [32000/60000 (53%)]\tLoss: 0.066413\n",
      "\n",
      "Test set: Average loss: 0.0457, Accuracy: 9858/10000 (99%)\n",
      "\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss: 0.093891\n",
      "Train Epoch: 10 [32000/60000 (53%)]\tLoss: 0.228374\n",
      "\n",
      "Test set: Average loss: 0.0430, Accuracy: 9867/10000 (99%)\n",
      "\n",
      "Train Epoch: 11 [0/60000 (0%)]\tLoss: 0.141574\n",
      "Train Epoch: 11 [32000/60000 (53%)]\tLoss: 0.173144\n",
      "\n",
      "Test set: Average loss: 0.0440, Accuracy: 9859/10000 (99%)\n",
      "\n",
      "Train Epoch: 12 [0/60000 (0%)]\tLoss: 0.137757\n",
      "Train Epoch: 12 [32000/60000 (53%)]\tLoss: 0.035225\n",
      "\n",
      "Test set: Average loss: 0.0469, Accuracy: 9847/10000 (98%)\n",
      "\n",
      "Train Epoch: 13 [0/60000 (0%)]\tLoss: 0.090944\n",
      "Train Epoch: 13 [32000/60000 (53%)]\tLoss: 0.197418\n",
      "\n",
      "Test set: Average loss: 0.0472, Accuracy: 9851/10000 (99%)\n",
      "\n",
      "Train Epoch: 14 [0/60000 (0%)]\tLoss: 0.145387\n",
      "Train Epoch: 14 [32000/60000 (53%)]\tLoss: 0.140212\n",
      "\n",
      "Test set: Average loss: 0.0687, Accuracy: 9783/10000 (98%)\n",
      "\n",
      "Train Epoch: 15 [0/60000 (0%)]\tLoss: 0.084234\n",
      "Train Epoch: 15 [32000/60000 (53%)]\tLoss: 0.159772\n",
      "\n",
      "Test set: Average loss: 0.0360, Accuracy: 9881/10000 (99%)\n",
      "\n",
      "Train Epoch: 16 [0/60000 (0%)]\tLoss: 0.084272\n",
      "Train Epoch: 16 [32000/60000 (53%)]\tLoss: 0.211205\n",
      "\n",
      "Test set: Average loss: 0.0420, Accuracy: 9873/10000 (99%)\n",
      "\n",
      "Train Epoch: 17 [0/60000 (0%)]\tLoss: 0.093839\n",
      "Train Epoch: 17 [32000/60000 (53%)]\tLoss: 0.083735\n",
      "\n",
      "Test set: Average loss: 0.1330, Accuracy: 9612/10000 (96%)\n",
      "\n",
      "Train Epoch: 18 [0/60000 (0%)]\tLoss: 0.170597\n",
      "Train Epoch: 18 [32000/60000 (53%)]\tLoss: 0.030371\n",
      "\n",
      "Test set: Average loss: 0.0351, Accuracy: 9886/10000 (99%)\n",
      "\n",
      "Train Epoch: 19 [0/60000 (0%)]\tLoss: 0.042844\n",
      "Train Epoch: 19 [32000/60000 (53%)]\tLoss: 0.121973\n",
      "\n",
      "Test set: Average loss: 0.0344, Accuracy: 9898/10000 (99%)\n",
      "\n",
      "Train Epoch: 20 [0/60000 (0%)]\tLoss: 0.096724\n",
      "Train Epoch: 20 [32000/60000 (53%)]\tLoss: 0.071256\n",
      "\n",
      "Test set: Average loss: 0.0346, Accuracy: 9886/10000 (99%)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def convert_image_np(inp):\n",
    "    \"\"\"Convert a Tensor to numpy image.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    return inp\n",
    "\n",
    "# We want to visualize the output of the spatial transformers layer\n",
    "# after the training, we visualize a batch of input images and\n",
    "# the corresponding transformed batch using STN.\n",
    "\n",
    "\n",
    "def visualize_stn():\n",
    "    with torch.no_grad():\n",
    "        # Get a batch of training data\n",
    "        data = next(iter(test_loader))[0].to(device)\n",
    "\n",
    "        input_tensor = data.cpu()\n",
    "        transformed_input_tensor = model.stn(data).cpu()\n",
    "\n",
    "        in_grid = convert_image_np(\n",
    "            torchvision.utils.make_grid(input_tensor))\n",
    "\n",
    "        out_grid = convert_image_np(\n",
    "            torchvision.utils.make_grid(transformed_input_tensor))\n",
    "\n",
    "        # Plot the results side-by-side\n",
    "        f, axarr = plt.subplots(1, 2)\n",
    "        axarr[0].imshow(in_grid)\n",
    "        axarr[0].set_title('Dataset Images')\n",
    "\n",
    "        axarr[1].imshow(out_grid)\n",
    "        axarr[1].set_title('Transformed Images')\n",
    "\n",
    "for epoch in range(1, 20 + 1):\n",
    "    train(epoch)\n",
    "    test()\n",
    "\n",
    "# Visualize the STN transformation on some input batch\n",
    "visualize_stn()\n",
    "\n",
    "plt.ioff()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
